On the Intersection of Self-Correction and Trust in Language Models

• URL: http://arxiv.org/abs/2311.02801v1
• Date: Mon, 6 Nov 2023 00:04:12 GMT
• Title: On the Intersection of Self-Correction and Trust in Language Models
• Authors: Satyapriya Krishna
• Abstract summary: Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex cognitive tasks. Recent research has explored the self-correction capabilities of LLMs to enhance their performance. We conduct experiments focusing on two key aspects of trustworthiness: truthfulness and toxicity.
• Score: 7.8833421052793256
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex cognitive tasks. However, their complexity and lack of transparency have raised several trustworthiness concerns, including the propagation of misinformation and toxicity. Recent research has explored the self-correction capabilities of LLMs to enhance their performance. In this work, we investigate whether these self-correction capabilities can be harnessed to improve the trustworthiness of LLMs. We conduct experiments focusing on two key aspects of trustworthiness: truthfulness and toxicity. Our findings reveal that self-correction can lead to improvements in toxicity and truthfulness, but the extent of these improvements varies depending on the specific aspect of trustworthiness and the nature of the task. Interestingly, our study also uncovers instances of "self-doubt" in LLMs during the self-correction process, introducing a new set of challenges that need to be addressed.

Related papers

• Fact-Level Confidence Calibration and Self-Correction [64.40105513819272]
  We propose a Fact-Level framework that calibrates confidence to relevance-weighted correctness at the fact level. We also develop Confidence-Guided Fact-level Self-Correction ($textbfConFix$), which uses high-confidence facts within a response as additional knowledge to improve low-confidence ones.
  arXiv  Detail & Related papers  (2024-11-20T14:15:18Z)
• Large Language Models have Intrinsic Self-Correction Ability [16.831123666582755]
  Large language models suffer from hallucinations that will cause performance degradation. One promising solution to improve the LLMs' performance is to ask LLMs to revise their answer after generation. In intrinsic self-correction is considered a promising direction because it does not utilize external knowledge.
  arXiv  Detail & Related papers  (2024-06-21T22:29:40Z)
• On the Intrinsic Self-Correction Capability of LLMs: Uncertainty and Latent Concept [36.27550578296276]
  Large Language Models (LLMs) are able to improve their responses when instructed to do so, a capability known as self-correction. In intrinsic self-correction is evident in various applications, but how and why it is effective remains unknown. We show that intrinsic self-correction can be progressively improved, allowing it to approach a converged state.
  arXiv  Detail & Related papers  (2024-06-04T14:55:43Z)
• When to Trust LLMs: Aligning Confidence with Response Quality [49.371218210305656]
  We propose CONfidence-Quality-ORDer-preserving alignment approach (CONQORD) It integrates quality reward and order-preserving alignment reward functions. Experiments demonstrate that CONQORD significantly improves the alignment performance between confidence and response accuracy.
  arXiv  Detail & Related papers  (2024-04-26T09:42:46Z)
• Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models [84.94220787791389]
  We propose Fact-and-Reflection (FaR) prompting, which improves the LLM calibration in two steps. Experiments show that FaR achieves significantly better calibration; it lowers the Expected Error by 23.5%. FaR even elicits the capability of verbally expressing concerns in less confident scenarios.
  arXiv  Detail & Related papers  (2024-02-27T01:37:23Z)
• Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models [23.42725642076256]
  Large Language Models (LLMs) have catalyzed an increasing interest in their self-correction capabilities. This paper presents a comprehensive investigation into the intrinsic self-correction of LLMs. We develop an "If-or-Else" (IoE) prompting framework, designed to guide LLMs in assessing their own "confidence"
  arXiv  Detail & Related papers  (2024-02-19T21:38:02Z)
• Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation [71.91287418249688]
  Large language models (LLMs) often struggle with factual inaccuracies, even when they hold relevant knowledge. We leverage the self-evaluation capability of an LLM to provide training signals that steer the model towards factuality. We show that the proposed self-alignment approach substantially enhances factual accuracy over Llama family models across three key knowledge-intensive tasks.
  arXiv  Detail & Related papers  (2024-02-14T15:52:42Z)
• Learning to Trust Your Feelings: Leveraging Self-awareness in LLMs for Hallucination Mitigation [9.730412606588335]
  We evaluate the ability of Large Language Models (LLMs) to discern and express their internal knowledge state. We propose a Reinforcement Learning from Knowledge Feedback (RLKF) training framework, leveraging reinforcement learning to enhance the factuality and honesty of LLMs.
  arXiv  Detail & Related papers  (2024-01-27T16:19:30Z)
• A Survey of Confidence Estimation and Calibration in Large Language Models [86.692994151323]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks in various domains. Despite their impressive performance, they can be unreliable due to factual errors in their generations. Assessing their confidence and calibrating them across different tasks can help mitigate risks and enable LLMs to produce better generations.
  arXiv  Detail & Related papers  (2023-11-14T16:43:29Z)
• N-Critics: Self-Refinement of Large Language Models with Ensemble of Critics [5.516095889257118]
  We propose a self-correction mechanism for Large Language Models (LLMs) to mitigate issues such as toxicity and fact hallucination. This method involves refining model outputs through an ensemble of critics and the model's own feedback.
  arXiv  Detail & Related papers  (2023-10-28T11:22:22Z)
• Large Language Models Cannot Self-Correct Reasoning Yet [78.16697476530994]
  Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities. Concerns persist regarding the accuracy and appropriateness of their generated content. A contemporary methodology, self-correction, has been proposed as a remedy to these issues.
  arXiv  Detail & Related papers  (2023-10-03T04:56:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.